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Mesh Generation for Aerospace Applications Sddhana, Wol. 16, Part 1, June 1991, pp. 1-45. © Printed in India. Mesh generation for aerospace applications N P WEATHERILL Institute for Numerical Methods in Engineering, University College of Swansea, Swansea SA2 8PP, UK MS received 18 January 1991 Abstract. In recent years there has been much research activity in the field of compressible flow simulation for aerodynamic applications. In the 1970's and 1980's the advances in the numerical solution of the Full Potential and Euler equations made, in principle, the inviscid flow simulation around complex aerodynamic shapes possible. At this stage much attention was focused on methods capable of generating meshes on which such calculations could be performed. In this paper an overview is presented of some techniques which have been developed to generate meshes for aerospace applications. Structured mesh generation techniques are discussed and their application to complicated shapes utilising the multiblock approach is highlighted. Unstructured mesh generation methods are also discussed with particular emphasis given to the Delaunay triangulation method. Finally, the advantages and disadvantages of the structured and unstructured approaches are discussed and new work is presented which attempts to utilise both these approaches in an efficient and flexible manner. Keywords. Structured mesh generation; unstructured mesh generation; inviscid flow simulation; meshes for aerospace applications; Delaunay triangulation method; computation fluid dynamics. 1. Structured meshes from partial differential equations 1.1 Introduction The majority of problems in physics and engineering can be described in terms of partial differential equations (Sneddon 1957). Moreover, many of these problems fall naturally into one of three physical categories: equilibrium problems, eigenvalue problems and propagation problems. At first it may appear inappropriate to suggest that before solving such problems by numerical methods a system of partial differential equations should be solved to determine the mesh! Not only is this approach viable but the generation of meshes from the solution of partial differential equations is today a popular approach. The properties of meshes generated by this approach are intimately connected to the properties of the partial differential equations used as the mesh generation equations. 1 2 N P" Weatherill 1.2 Elliptic systems 1.2a Laplace and Poisson equations: The motivation for the use of elliptic equations as generators of mesh points can be derived from a number of sources. The nature of elliptic equations is to smooth boundary data and this affords a most desirable property. In fact, many elliptic equations are based around Laplace's equation which is well known as a filter or smoothing operator. In two dimensions, Laplace's equation with Cauchy-Riemann type boundary conditions can be used to generate conformal mappings (see § 3). In fact, the real and imaginary parts of an analytic transformation are harmonic functions. An alternative viewpoint, and one which is most appropriate in computational fluid dynamics, is to note that a two-dimensional, inviscid, steady, incompressible flow is described in terms of Laplace's equation in the potential function q) or the stream function ~,, i.e. ~2tp/~X2 -t- ~2(p/0y2 = q)xx -1- q)yy = 0 or c?2d//Ox2 + 020/OY 2 = Oxx + O. = O. (1) Given appropriate boundary conditions for (1) the solution represents the streamlines and potential lines of the flowfield, To utilise these ideas for mesh generation, it proves more convenient to transform these equations so that x and y become the dependent variables. In such a case it is then possible to apply boundary conditions to x and y which, in general, will be the known boundary coordinate values of the geometrical domain. If (1) are generalised to include source functions P and Q and, for notational convenience, ~ and t/replace ~0 and O, respectively, then (1) can be written as Cxx + ¢. = P(¢, t/), r/xx + ~/y, = Q(~, r/), (2) where = ¢(x,y), (2a) and r/= ~/(x, y). (2b) Hence, using these relations leads to the system of coupled nonlinear equations in x and y, namely, o~x¢¢ - 2flx¢. + 7x,i,~ = - J2(xcP + x~Q), ¢ty¢¢ -- 2fly¢, + yy.~ = - j2(yCp + y,Q), (3) with ~(x2 + y~), fl = (x~x~ + y~y~), ~ = (x~ + y~). (4) Equations (3) with (4), which were derived and popularised by Thompson et al (1974), are the transformed equations and form the system of mesh generation equations subject to applied boundary conditions. It is noted that ~t, fl and y are the metric coefficients and if fl = 0 everywhere the mesh is orthogonal. There is a temptation to substitute/~ = 0 in (3). However, this does not impose the orthogonality condition, and in fact, can lead to cross-over within the mesh for some boundary shapes. The solution of this system of equations can be obtained by using an appropriate linearisation and central difference representation for the derivatives. For P = Q = 0, Mesh generation for aerospace applications 3 the residual on a square mesh with ~ = ih, (i = 0, 1, 2 ..... m - 1, m), ~/=jh, (j = 0, 1, 2 ..... n- 1, n) can be represented as Ri,j" --- °ti,j(ul" + l,j -- 2u~+uT-1, j)--2fli,j(un+l,j+l --Un-l,j+l -- - uT+ 1,~- 1 + uT- ld- 1) + 2)i,~(u7,j+ ~ - 2ui~4 + ui~,~- 1) (5) with O~i,j=(Xi,j+" 1 -x~,j_~)n 2 +(~,~+~ __ ~i,j_ 1)2, fli,j = (X~+ 1,j -- xn- 1 ,j)(Xin, j+ l -- xinj-1 ) + (Y~+ 1,j -- ~i- l,j)(YT, j+ 1 -- Y~i,j- 1 )' 2),,~= (xLl,j -- x~_~,j)2n "[- (Yi+. 1,j -- YT- 1,j) 2' (6) and where u = (x,y) and u .~. represents the unknown at the point (i,j) at iteration level n. It follows that the solution to (3) can be obtained using the stationary and linear successive point over-relaxation scheme, u,~,~.1 = uT, j + coR~,/2(~ + y), (7) where co is the relaxation parameter, defined in the interval [1, 2]. A similar procedure is adopted to include the source functions. Other solution routines can be used including successive line over-relaxation, approximate factorisation and alternating direction implicit schemes. The convergence can be accelerated by using multiple grids (Forsey & Billing 1988; Thames 1984). The ideas expressed here extend into three dimensions. The system of Poisson equations is ~xx + ~,, + ~ = P(~. tl. O, ~,,,,+ ~,, + ~= = Q(~, 7, O, (xx + (,, + ~zz = R(~,q, O, (8) in which P, Q, and R are now the source terms. Transforming the variables in (8) leads to = _ j2 (Pr¢ + Qr, + Rr;). (9) where r = (x, y, z) T, a,j = yy,.~2)=j, 2:o is the ijth cofactor of the matrix M = y~ y, Z~ g~ and the Jacobian d is the determinant of M. Central difference representation of the derivatives and relaxation techniques can be used for the solution of (9). 1.2b Grid control functions: The inherent smoothing properties of Laplace's equation ensures that, in the absence of boundary curvature, the mesh points are evenly spaced. However, near convex boundaries the grid points become more closely spaced, whilst near concave boundaries the mesh spacing is more sparse (figure 1). 4 N P Weatherill Figure 1. Mesh point spacing on concave and convex boundaries. These properties are not always desirable for mesh generation where it is essential that the mesh near a boundary reflects the shape of that boundary. Control of mesh point spacing can be achieved by introducing source terms into Laplace's equation thus converting the system of equations into Poisson type. The source terms, often called grid control functions, can be so designed as to provide the flexibility required to control mesh point spacing. Typical effects of the source functions P and Q in two dimensions are illustrated in figure 2. The major problem for automatic mesh generation is how to choose the control functions and, in some cases, the inherent parameters associated with them. Automatic methods have been developed (Sorenson & Steger 1979) but one with a particularly beneficial formulation is that due to Thomas & Middlecoff (1980). The basic idea behind this approach is to utilise the distribution of points on a boundary to compute the forcing functions which are then interpolated into the field. Using the elliptic equations, and assuming that all second derivatives transverse to the boundary are zero, yields the following equations P = - (x~x~¢ + y~y~)/(x~ + y~), along r/= constant boundaries, and Q = - (x~x~ + y~yn~)/(x 2 + y2), along ~ = constant boundaries. Hence, given a suitable boundary point distribution the control functions can be calculated to reflect this spacing in the field. The method readily extends into three dimensions, where the boundary points on the surfaces are used to generate the boundary values of the three control functions. 1.2c Other elliptic equations: The use of the Laplace and Poisson equations for mesh generation is now widespread. Several modifications to the basic system described in § 1.2 have been used (Thompson et al 1985). However, other different elliptic systems have been investigated and used successfully. Boerstoel (1988) has chosen to modify the Poisson formulation to include positive weight functions that are chosen to be inversely proportional to desired grid point distances along grid lines. Schwarz (1986) has extended the ideas on elliptic systems to include higher-order grid generation systems. Fourth-order (biharmonic) systems are implemented as a set Figure 2. Effectsof source functionsP and Q on the mesh. Mesh 9eneration for aerospace applications 5 of two second-order equations (Poisson's and Laplace's) whilst a sixth-order system is solved as a system of three second-order equations. The advantage of the higher- order systems is that they allow two and three boundary conditions to be specified for the fourth-order and sixth-order equations, respectively.
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